Optimization of air pollution measurements with unmanned aerial vehicle low-cost sensor based on an inductive knowledge management method

The article presents the study of Particulate Matter air pollution with PM 1 , PM 2,5 and PM 10 by means of a low-cost sensors mounted on Unmanned Aerial Vehicles. The article is divided into two parts. In first part pollution measurement system is described. In second part expert system for optimization of flight parameters is described. The research was conducted over a municipal cemetery area in Poland. The obtained results were analyzed through an inductive knowledge management system (decision tree method) for classification analysis of air pollution. The decision tree mechanism would be used to optimize flight parameters taking into account the air pollution parameters. The analysis was made from the influence of PM concentration point of view, depending on the altitude. The decision tree method was used, which allowed to determine, among other aspects, which PM indicator should be measured and which altitude plays a greater role in the optimization of air pollution measurements by means of cheap sensors mounted on drones. As a result of the analysis, the optimum flight altitude of the measurement drone in the specified area was determined.